Summary: Adaptive Mesh Refinement (AMR) methods are widespread
in scientific computing, and visualizing the resulting data with
efficient and accurate rendering methods can be vital for enabling
interactive data exploration.
In this work, we
detail a comprehensive solution for directly volume rendering block-structured
(Berger-Colella) AMR data in the
OSPRay interactive CPU ray tracing framework. In particular, we
contribute a general method for representing and traversing AMR data
using a kd-tree structure, and four different reconstruction
options, one of which in particular (the basis function approach)
is novel compared to existing methods. We demonstrate our system on two
types of block-structured AMR data and compressed scalar
field data, and show how it can be easily used in existing production-ready
applications through a prototypical integration in the widely used visualization program ParaView.

Summary: We present novel transfer functions that advance the classification of volume data by combining the advantages of the existing boundary-based and structure-based methods. We introduce the usage of the standard deviation of ambient occlusion to quantify the variation of both boundary and structure information across voxels, and name our method as boundary-structure-aware transfer functions. Our method gives concrete guidelines to better reveal the interior and exterior structures of features, especially for occluded objects without perfect homogeneous intensities. Furthermore, our method separates these patterns from other materials that may contain similar average intensities, but with different intensity variations. The proposed method extends the expressiveness and the utility of volume rendering in extracting the continuously changed patterns and achieving more robust volume classifications.

Author(s): Lina Yu, University of Nebraska-Lincoln
Hongfeng Yu, University of Nebraska-Lincoln

Summary: Numerical simulation results generated from high performance computing (HPC) environments have become extremely concurrent with the recent advances in computer simulation technology, and there is an increase in the demand for extra-scale visualization techniques. In this paper, we propose a parallel particle-based volume rendering method based on adaptive particle size adjustment technique, which is suitable for handling large-scale and complex distributed volume datasets in the HPC environment. In the experiment, the proposed technique is applied to a large-scale unstructured thermal fluid simulation, and a performance model is constructed to confirm the effectiveness of the proposed technique.